Abstract:

The goal of many single-cell omics studies is to gain insight into the complicated and large network of biochemical reactions that control cell fate and state. However, these data are often analyzed by purely statistical methods that are not formally connected to the biochemical kinetics and dynamical systems inside cells. We introduce a method for inferring a structural component, the stoichiometric subspace, of a biochemical reaction network using single-cell omics data. Our approach leverages the geometric theory of toric varieties, and connects aspects of Chemical Reaction Network Theory to data analysis and statistics. We validate our method on a public dataset of cultured cells subjected to various drug treatments.

Short Bio:

Shu Wang has been Assistant Professor at the Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto since January, 2024. He completed his PhD in Biophysics at Harvard U in 2021, and was Postdoctoral Associate at MIT from 2021-2023. His research interests are broadly in trying to mathematically understand the multi-scale networks underlying biological systems (e.g. protein signaling, cell states, tissue organization), seeking the accuracy and precision that would be needed to reliably treat heterogeneous diseases such as cancer.